LARS is a type of forward step-wise or iterative regression[1]}, [2]}. In this case, all model coefficients are initialized to zero and the covariate (i.e., polynomial values) most correlated to the error residual is determined (i.e., those having the most significant impact on the fit). The corresponding ChIMES parameter is modified incrementally to minimize the error residual until a second covariate yields an equal correlation. At this point, it is included in the active parameter set and both coefficients are modified simultaneously. The process continues until all coefficients are included in the solution, at which point a result equivalent to ordinary least squares fitting is obtained. In practice, LARS steps using a subset of all possible parameters are chosen.
